Version history

{
  "scope": "mouse cortex (see paper for region/cell-type detail)",
  "claim_text": "Whole-cortex in situ sequencing reveals input-dependent area identity. Here we used BARseq, a high-throughput in situ sequencing technique, to interrogate the expression of 104 cell-type marker genes in 10.3 million cells, including 4,194,658 cortica",
  "raw_fields": {
    "n": null,
    "doi": "10.1038/s41586-024-07221-6",
    "claim": "Whole-cortex in situ sequencing reveals input-dependent area identity. Here we used BARseq, a high-throughput in situ sequencing technique, to interrogate the expression of 104 cell-type marker genes in 10.3 million cells, including 4,194,658 cortica",
    "cite_key": "Chen2025a",
    "evidence": "Whole-cortex in situ sequencing reveals input-dependent area identity. — The cerebral cortex is composed of neuronal types with diverse gene expression that are organized into specialized cortical areas. These areas, each with characteristic cytoarchitecture, connectivityand neuronal activity, are wired into modular networks. However, it remains unclear whether these spa",
    "effect_size": null,
    "text_access": "abstract_only",
    "study_system": "mouse cortex (see paper for region/cell-type detail)",
    "argument_role": "supporting",
    "replication_status": "single_study",
    "claim_source_sentence": "Here we used BARseq, a high-throughput in situ sequencing technique, to interrogate the expression of 104 cell-type marker genes in 10.3 million cells, including 4,194,658 cortical neurons over nine mouse forebrain hemispheres, at cellular resolution.",
    "source_provenance_status": "non_substring_match",
    "replication_evidence_dois": [],
    "effect_size_source_sentence": null
  },
  "section_id": "section_11",
  "source_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_11_evidence_package.json",
  "effect_size": null,
  "review_repo": "ComputationalReviewRecurrence",
  "section_ref": "wiki_page:computationalreviewrecurrence-11-pattern-completion",
  "source_kind": "review_finding",
  "source_path": "evidence/section_11_evidence_package.json",
  "source_refs": [
    "paper:paper-014dc32032c7"
  ],
  "source_span": "Here we used BARseq, a high-throughput in situ sequencing technique, to interrogate the expression of 104 cell-type marker genes in 10.3 million cells, including 4,194,658 cortical neurons over nine mouse forebrain hemispheres, at cellular resolution.",
  "study_system": "mouse cortex (see paper for region/cell-type detail)",
  "evidence_refs": [
    {
      "ref": "paper:paper-014dc32032c7"
    }
  ],
  "section_title": "11. Physiological signature III — pattern completion, replay, and sequence generation as recurrent-circuit read-outs in mouse cortex",
  "source_policy": {
    "mode": "public_source_pointer_with_short_context",
    "notes": [
      "Local review repositories are read-only inputs.",
      "SciDEX stores paper metadata, structured evidence, file pointers, and short citation contexts; it does not copy full review prose."
    ],
    "source_commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
    "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
  },
  "evidence_summary": "Whole-cortex in situ sequencing reveals input-dependent area identity. — The cerebral cortex is composed of neuronal types with diverse gene expression that are organized into specialized cortical areas. These areas, each with characteristic cytoarchitecture, connectivityand neuronal activity, are wired into modular networks. However, it remains unclear whether these spa",
  "review_bundle_ref": "analysis_bundle:ab-d9c479db9be9",
  "replication_status": "single_study",
  "review_package_ref": "analysis_bundle:ab-d9c479db9be9",
  "source_artifact_ref": "wiki_page:computationalreviewrecurrence-11-pattern-completion",
  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_11_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
  "created_by": "persona-jerome-lecoq-gbo-neuroscience",
  "repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence"
}